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Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

A long-standing question in theoretical computer science is to offer analysis tools for improving performance on typical everyday, non-worst-case instances. A recent model addressing this goal is the algorithms-with-predictions model: each problem instance comes with a possibly error-prone prediction, and the worst-case running time is given as a function of the error in that prediction. This model reflects how recent advances in machine learning are able to make reasonably good predictions on practical ‒ even very complex ‒ datasets. The algorithms-with-predictions model has been used to give strong approximation-ratio guarantees for fundamental online algorithms like scheduling and caching. How to leverage predictions to speed up running time of offline algorithms and data structures has received less attention. This Dagstuhl Seminar aims to bring together researchers from the data structures, combinatorial optimization and learned predictions communities to address the challenges of adopting learned predictions for improving running time guarantees.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Computational geometry is concerned with the design, analysis, and implementation of algorithms for geometric and topological problems, which arise naturally in a wide range of areas, including computer graphics, CAD, robotics, computer vision, image processing, spatial databases, GIS, molecular biology, sensor networks, machine learning, data mining, scientific computing, theoretical computer science, and pure mathematics. Computational geometry is a vibrant and mature field of research, with several dedicated international conferences and journals and strong intellectual connections with other computing and mathematics disciplines. The emphasis of the Dagstuhl Seminar is on presenting recent developments in computational geometry, as well as identifying new challenges, opportunities, and connections to other fields of computing.

Trier University, Computer Science Department

WG conferences aim to connect theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. This conference series has a long tradition and bridges discrete mathematics and computer science. WG is mainly concerned with efficient algorithms of various types (e.g., sequential, parallel, distributed, randomized, parameterized) for problems on graphs and networks. The goal is to present recent results and to identify and explore directions for future research.

PC Chairs;     Tel.: [(not so relevant)];     Email: fernau@uni-trier.de; kindermann@uni-trier.de

design and analysis of sequential, parallel, randomized, parameterized algorithms, distributed graph and network algorithms, structural graph theory with algorithmic or complexity applications, computational complexity of graph and network problems, graph grammars, graph rewriting systems and graph modeling, graph drawing and layouts, computational geometry, computational biology, graph mining, random graphs and models of the web and scale-free networks, support of the above concepts by suitable implementations and applications.

Trier University, Computer Science Department

WG conferences aim to connect theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. This conference series has a long tradition and bridges discrete mathematics and computer science. WG is mainly concerned with efficient algorithms of various types (e.g., sequential, parallel, distributed, randomized, parameterized) for problems on graphs and networks. The goal is to present recent results and to identify and explore directions for future research.

PC Chairs;     Tel.: [(not so relevant)];     Email: fernau@uni-trier.de; kindermann@uni-trier.de

design and analysis of sequential, parallel, randomized, parameterized algorithms, distributed graph and network algorithms, structural graph theory with algorithmic or complexity applications, computational complexity of graph and network problems, graph grammars, graph rewriting systems and graph modeling, graph drawing and layouts, computational geometry, computational biology, graph mining, random graphs and models of the web and scale-free networks, support of the above concepts by suitable implementations and applications.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Constraint optimization is today a de facto practical approach to efficiently solving various types of NP-hard optimization problems arising from real-world settings. This is primarily due to significant advances in practical algorithms and implementation-level techniques, which have resulted in increasingly efficient and robust constraint optimization solvers for a range of NP-hard declarative languages. However, the need for even more efficient and robust constraint optimization solvers continues to grow, as in several contexts more and more complex optimization problems need to be solved.